{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.10","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2023-05-21T01:26:56.626382Z","iopub.execute_input":"2023-05-21T01:26:56.627006Z","iopub.status.idle":"2023-05-21T01:27:00.537833Z","shell.execute_reply.started":"2023-05-21T01:26:56.626949Z","shell.execute_reply":"2023-05-21T01:27:00.535936Z"},"trusted":true},"execution_count":18,"outputs":[{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","Cell \u001b[0;32mIn[18], line 12\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;66;03m# Input data files are available in the read-only \"../input/\" directory\u001b[39;00m\n\u001b[1;32m      9\u001b[0m \u001b[38;5;66;03m# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\u001b[39;00m\n\u001b[1;32m     11\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mos\u001b[39;00m\n\u001b[0;32m---> 12\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m dirname, _, filenames \u001b[38;5;129;01min\u001b[39;00m os\u001b[38;5;241m.\u001b[39mwalk(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m/kaggle/input\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[1;32m     13\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m filename \u001b[38;5;129;01min\u001b[39;00m filenames:\n\u001b[1;32m     14\u001b[0m         \u001b[38;5;28mprint\u001b[39m(os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(dirname, filename))\n","File \u001b[0;32m/opt/conda/lib/python3.10/os.py:419\u001b[0m, in \u001b[0;36m_walk\u001b[0;34m(top, topdown, onerror, followlinks)\u001b[0m\n\u001b[1;32m    414\u001b[0m         \u001b[38;5;66;03m# Issue #23605: os.path.islink() is used instead of caching\u001b[39;00m\n\u001b[1;32m    415\u001b[0m         \u001b[38;5;66;03m# entry.is_symlink() result during the loop on os.scandir() because\u001b[39;00m\n\u001b[1;32m    416\u001b[0m         \u001b[38;5;66;03m# the caller can replace the directory entry during the \"yield\"\u001b[39;00m\n\u001b[1;32m    417\u001b[0m         \u001b[38;5;66;03m# above.\u001b[39;00m\n\u001b[1;32m    418\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m followlinks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m islink(new_path):\n\u001b[0;32m--> 419\u001b[0m             \u001b[38;5;28;01myield from\u001b[39;00m _walk(new_path, topdown, onerror, followlinks)\n\u001b[1;32m    420\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    421\u001b[0m     \u001b[38;5;66;03m# Recurse into sub-directories\u001b[39;00m\n\u001b[1;32m    422\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m new_path \u001b[38;5;129;01min\u001b[39;00m walk_dirs:\n","File \u001b[0;32m/opt/conda/lib/python3.10/os.py:419\u001b[0m, in \u001b[0;36m_walk\u001b[0;34m(top, topdown, onerror, followlinks)\u001b[0m\n\u001b[1;32m    414\u001b[0m         \u001b[38;5;66;03m# Issue #23605: os.path.islink() is used instead of caching\u001b[39;00m\n\u001b[1;32m    415\u001b[0m         \u001b[38;5;66;03m# entry.is_symlink() result during the loop on os.scandir() because\u001b[39;00m\n\u001b[1;32m    416\u001b[0m         \u001b[38;5;66;03m# the caller can replace the directory entry during the \"yield\"\u001b[39;00m\n\u001b[1;32m    417\u001b[0m         \u001b[38;5;66;03m# above.\u001b[39;00m\n\u001b[1;32m    418\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m followlinks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m islink(new_path):\n\u001b[0;32m--> 419\u001b[0m             \u001b[38;5;28;01myield from\u001b[39;00m _walk(new_path, topdown, onerror, followlinks)\n\u001b[1;32m    420\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    421\u001b[0m     \u001b[38;5;66;03m# Recurse into sub-directories\u001b[39;00m\n\u001b[1;32m    422\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m new_path \u001b[38;5;129;01min\u001b[39;00m walk_dirs:\n","File \u001b[0;32m/opt/conda/lib/python3.10/os.py:419\u001b[0m, in \u001b[0;36m_walk\u001b[0;34m(top, topdown, onerror, followlinks)\u001b[0m\n\u001b[1;32m    414\u001b[0m         \u001b[38;5;66;03m# Issue #23605: os.path.islink() is used instead of caching\u001b[39;00m\n\u001b[1;32m    415\u001b[0m         \u001b[38;5;66;03m# entry.is_symlink() result during the loop on os.scandir() because\u001b[39;00m\n\u001b[1;32m    416\u001b[0m         \u001b[38;5;66;03m# the caller can replace the directory entry during the \"yield\"\u001b[39;00m\n\u001b[1;32m    417\u001b[0m         \u001b[38;5;66;03m# above.\u001b[39;00m\n\u001b[1;32m    418\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m followlinks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m islink(new_path):\n\u001b[0;32m--> 419\u001b[0m             \u001b[38;5;28;01myield from\u001b[39;00m _walk(new_path, topdown, onerror, followlinks)\n\u001b[1;32m    420\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    421\u001b[0m     \u001b[38;5;66;03m# Recurse into sub-directories\u001b[39;00m\n\u001b[1;32m    422\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m new_path \u001b[38;5;129;01min\u001b[39;00m walk_dirs:\n","File \u001b[0;32m/opt/conda/lib/python3.10/os.py:377\u001b[0m, in \u001b[0;36m_walk\u001b[0;34m(top, topdown, onerror, followlinks)\u001b[0m\n\u001b[1;32m    374\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[1;32m    376\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 377\u001b[0m     is_dir \u001b[38;5;241m=\u001b[39m \u001b[43mentry\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mis_dir\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    378\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m:\n\u001b[1;32m    379\u001b[0m     \u001b[38;5;66;03m# If is_dir() raises an OSError, consider that the entry is not\u001b[39;00m\n\u001b[1;32m    380\u001b[0m     \u001b[38;5;66;03m# a directory, same behaviour than os.path.isdir().\u001b[39;00m\n\u001b[1;32m    381\u001b[0m     is_dir \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "],"ename":"KeyboardInterrupt","evalue":"","output_type":"error"}]},{"cell_type":"code","source":"import torch\nimport torchvision\nimport torchvision.transforms as transforms\n\ntransform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\nbatch_size = 32\ntrain_dataset = torchvision.datasets.ImageFolder(\"/kaggle/input/cifar100/CIFAR100/TRAIN\", transform=transform)\ntest_dataset = torchvision.datasets.ImageFolder(\"/kaggle/input/cifar100/CIFAR100/TEST\", transform=transform)\ntrain_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=2)\ntest_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=False, num_workers=2)","metadata":{"execution":{"iopub.status.busy":"2023-05-21T03:28:02.053030Z","iopub.execute_input":"2023-05-21T03:28:02.053723Z","iopub.status.idle":"2023-05-21T03:28:02.505602Z","shell.execute_reply.started":"2023-05-21T03:28:02.053685Z","shell.execute_reply":"2023-05-21T03:28:02.504584Z"},"trusted":true},"execution_count":29,"outputs":[]},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nimport numpy as np\n\n# functions to show an image\n\n\ndef imshow(img):\n    img = img / 2 + 0.5     # unnormalize\n    npimg = img.numpy()\n    plt.imshow(np.transpose(npimg, (1, 2, 0)))\n    plt.show()\n\n\n# get some random training images\ndataiter = iter(train_loader)\n# images, labels = dataiter.next()\nimages, labels = next(dataiter)\n\n# show images\nimshow(torchvision.utils.make_grid(images))\n# print labels\nprint(labels.shape)","metadata":{"execution":{"iopub.status.busy":"2023-05-21T03:28:04.057748Z","iopub.execute_input":"2023-05-21T03:28:04.058326Z","iopub.status.idle":"2023-05-21T03:28:04.703119Z","shell.execute_reply.started":"2023-05-21T03:28:04.058282Z","shell.execute_reply":"2023-05-21T03:28:04.700105Z"},"trusted":true},"execution_count":30,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}},{"name":"stdout","text":"torch.Size([32])\n","output_type":"stream"}]},{"cell_type":"code","source":"import math\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.nn import init\n\ndef conv3x3(in_planes, out_planes, stride=1):\n    \"3x3 convolution with padding\"\n    return nn.Conv2d(\n        in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False\n    )\n\n\nclass BasicBlock(nn.Module):\n    expansion = 1\n\n    def __init__(\n        self, inplanes, planes, stride=1, downsample=None, use_triplet_attention=False\n    ):\n        super(BasicBlock, self).__init__()\n        self.conv1 = conv3x3(inplanes, planes, stride)\n        self.bn1 = nn.BatchNorm2d(planes)\n        self.relu = nn.ReLU(inplace=True)\n        self.conv2 = conv3x3(planes, planes)\n        self.bn2 = nn.BatchNorm2d(planes)\n        self.downsample = downsample\n        self.stride = stride\n\n        if use_triplet_attention:\n            self.triplet_attention = TripletAttention(planes, 16)\n        else:\n            self.triplet_attention = None\n\n    def forward(self, x):\n        residual = x\n\n        out = self.conv1(x)\n        out = self.bn1(out)\n        out = self.relu(out)\n\n        out = self.conv2(out)\n        out = self.bn2(out)\n\n        if self.downsample is not None:\n            residual = self.downsample(x)\n\n        if not self.triplet_attention is None:\n            out = self.triplet_attention(out)\n\n        out += residual\n        out = self.relu(out)\n\n        return out\n      \nclass Bottleneck(nn.Module):\n    expansion = 4\n\n    def __init__(\n        self, inplanes, planes, stride=1, downsample=None, use_triplet_attention=False\n    ):\n        super(Bottleneck, self).__init__()\n        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)\n        self.bn1 = nn.BatchNorm2d(planes)\n        self.conv2 = nn.Conv2d(\n            planes, planes, kernel_size=3, stride=stride, padding=1, bias=False\n        )\n        self.bn2 = nn.BatchNorm2d(planes)\n        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)\n        self.bn3 = nn.BatchNorm2d(planes * 4)\n        self.relu = nn.ReLU(inplace=True)\n        self.downsample = downsample\n        self.stride = stride\n\n        if use_triplet_attention:\n            self.triplet_attention = TripletAttention(planes * 4, 16)\n        else:\n            self.triplet_attention = None\n\n    def forward(self, x):\n        residual = x\n\n        out = self.conv1(x)\n        out = self.bn1(out)\n        out = self.relu(out)\n\n        out = self.conv2(out)\n        out = self.bn2(out)\n        out = self.relu(out)\n\n        out = self.conv3(out)\n        out = self.bn3(out)\n\n        if self.downsample is not None:\n            residual = self.downsample(x)\n\n        if not self.triplet_attention is None:\n            out = self.triplet_attention(out)\n\n        out += residual\n        out = self.relu(out)\n\n        return out\n\nclass Res2NetBottleneck(nn.Module):\n\n    expansion = 4\n\n    def __init__(\n        self, inplanes, planes, stride=1, downsample=None, use_triplet_attention=False, scales=4\n    ):\n        super(Res2NetBottleneck, self).__init__()\n        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, stride=stride, bias=False)\n        self.bn1 = nn.BatchNorm2d(planes)\n        self.conv2 = nn.ModuleList([nn.Conv2d(planes//scales, planes//scales, kernel_size=3, padding=1, bias=False) for _ in range(scales-1)])\n        self.bn2 = nn.ModuleList([nn.BatchNorm2d(planes // scales) for _ in range(scales-1)])\n        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)\n        self.bn3 = nn.BatchNorm2d(planes * 4)\n        self.relu = nn.ReLU(inplace=True)\n        self.downsample = downsample\n        self.stride = stride\n        self.scales = scales\n\n        if use_triplet_attention:\n            self.triplet_attention = TripletAttention(planes * 4, 16)\n        else:\n            self.triplet_attention = None\n\n    def forward(self, x):\n        residual = x\n\n        out = self.conv1(x)\n        out = self.bn1(out)\n        out = self.relu(out)\n        \n#         print(out.shape)\n#         print(\"Begin Forward\")\n        xs = torch.chunk(out, self.scales, 1)\n        ys = []\n        \n        for s in range(self.scales):\n            if s == 0:\n                ys.append(xs[s])\n            elif s == 1:\n                ys.append(self.relu(self.bn2[s-1](self.conv2[s-1](xs[s]))))\n            else:\n#                 print(xs[s].shape, ys[-1].shape)\n                ys.append(self.relu(self.bn2[s-1](self.conv2[s-1](xs[s] + ys[-1]))))\n        out = torch.cat(ys, 1)\n\n        out = self.conv3(out)\n        out = self.bn3(out)\n\n        if self.downsample is not None:\n            residual = self.downsample(x)\n\n        if not self.triplet_attention is None:\n            out = self.triplet_attention(out)\n\n        out += residual\n        out = self.relu(out)\n\n        return out    \n\nclass ColaBasicBlock(nn.Module):\n\n    expansion = 4\n\n    def __init__(\n        self, inplanes, planes, stride=1, downsample=None, use_triplet_attention=False, scales=4\n    ):\n        super(ColaBasicBlock, self).__init__()\n        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, stride=stride, bias=False)\n        self.bn1 = nn.BatchNorm2d(planes)\n        self.mix = nn.ModuleList([nn.Conv2d(planes, 1, kernel_size=1, stride=1, padding=1, bias=False), \n                                  nn.BatchNorm2d(1),\n                                  nn.Conv2d(1, planes, kernel_size=1, stride=1, padding=1, bias=False),\n                                  nn.BatchNorm2d(planes),\n                                  nn.ReLU(inplace=True)])\n        self.conv2 = nn.ModuleList([nn.Conv2d(planes//scales, planes//scales, kernel_size=3, padding=1, bias=False) for _ in range(scales-1)])\n        self.bn2 = nn.ModuleList([nn.BatchNorm2d(planes // scales) for _ in range(scales-1)])\n        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)\n        self.bn3 = nn.BatchNorm2d(planes * 4)\n        self.relu = nn.ReLU(inplace=True)\n        self.downsample = downsample\n        self.stride = stride\n        self.scales = scales\n\n        if use_triplet_attention:\n            self.triplet_attention = TripletAttention(planes * 4, 16)\n        else:\n            self.triplet_attention = None\n\n    def forward(self, x):\n        residual = x\n\n        out = self.conv1(x)\n        out = self.bn1(out)\n        out = self.relu(out)\n        \n#         print(out.shape)\n#         print(\"Begin Forward\")\n        xs = torch.chunk(out, self.scales, 1)\n        ys = []\n        \n        for s in range(self.scales):\n            if s == 0:\n                ys.append(xs[s])\n            elif s == 1:\n                ys.append(self.relu(self.bn2[s-1](self.conv2[s-1](xs[s]))))\n            else:\n#                 print(xs[s].shape, ys[-1].shape)\n                ys.append(self.relu(self.bn2[s-1](self.conv2[s-1](xs[s] + ys[-1]))))\n        out = torch.cat(ys, 1)\n\n        out = self.conv3(out)\n        out = self.bn3(out)\n\n        if self.downsample is not None:\n            residual = self.downsample(x)\n\n        if not self.triplet_attention is None:\n            out = self.triplet_attention(out)\n\n        out += residual\n        out = self.relu(out)\n\n        return out    \n    \nclass ResNet(nn.Module):\n    def __init__(self, block, layers, network_type, num_classes, att_type=None):\n        self.inplanes = 64\n        super(ResNet, self).__init__()\n        self.network_type = network_type\n        # different model config between ImageNet and CIFAR\n        if network_type == \"ImageNet\":\n            self.conv1 = nn.Conv2d(\n                3, 64, kernel_size=7, stride=2, padding=3, bias=False\n            )\n            self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n            self.avgpool = nn.AvgPool2d(7)\n        else:\n            self.conv1 = nn.Conv2d(\n                3, 64, kernel_size=3, stride=1, padding=1, bias=False\n            )\n\n        self.bn1 = nn.BatchNorm2d(64)\n        self.relu = nn.ReLU(inplace=True)\n\n        self.layer1 = self._make_layer(block, 64, layers[0], att_type=att_type)\n        self.layer2 = self._make_layer(\n            block, 128, layers[1], stride=2, att_type=att_type\n        )\n        self.layer3 = self._make_layer(\n            block, 256, layers[2], stride=2, att_type=att_type\n        )\n        self.layer4 = self._make_layer(\n            block, 512, layers[3], stride=2, att_type=att_type\n        )\n\n        self.fc = nn.Linear(512 * block.expansion, num_classes)\n\n        init.kaiming_normal_(self.fc.weight)\n        for key in self.state_dict():\n            if key.split(\".\")[-1] == \"weight\":\n                if \"conv\" in key:\n                    init.kaiming_normal_(self.state_dict()[key], mode=\"fan_out\")\n                if \"bn\" in key:\n                    if \"SpatialGate\" in key:\n                        self.state_dict()[key][...] = 0\n                    else:\n                        self.state_dict()[key][...] = 1\n            elif key.split(\".\")[-1] == \"bias\":\n                self.state_dict()[key][...] = 0\n\n    def _make_layer(self, block, planes, blocks, stride=1, att_type=None):\n        downsample = None\n        if stride != 1 or self.inplanes != planes * block.expansion:\n            downsample = nn.Sequential(\n                nn.Conv2d(\n                    self.inplanes,\n                    planes * block.expansion,\n                    kernel_size=1,\n                    stride=stride,\n                    bias=False,\n                ),\n                nn.BatchNorm2d(planes * block.expansion),\n            )\n\n        layers = []\n        layers.append(\n            block(\n                self.inplanes,\n                planes,\n                stride,\n                downsample,\n                use_triplet_attention=att_type == \"TripletAttention\",\n            )\n        )\n        self.inplanes = planes * block.expansion\n        for i in range(1, blocks):\n            layers.append(\n                block(\n                    self.inplanes,\n                    planes,\n                    use_triplet_attention=att_type == \"TripletAttention\",\n                )\n            )\n\n        return nn.Sequential(*layers)\n\n    def forward(self, x):\n        x = self.conv1(x)\n        x = self.bn1(x)\n        x = self.relu(x)\n        if self.network_type == \"ImageNet\":\n            x = self.maxpool(x)\n\n        x = self.layer1(x)\n#         print(\"Begin layer2\")\n        x = self.layer2(x)\n        x = self.layer3(x)\n        x = self.layer4(x)\n\n        if self.network_type == \"ImageNet\":\n            x = self.avgpool(x)\n        else:\n            x = F.avg_pool2d(x, 4)\n        x = x.view(x.size(0), -1)\n        x = self.fc(x)\n        return x\n\n\ndef ResidualNet(network_type, depth, num_classes, att_type):\n\n    assert network_type in [\n        \"ImageNet\",\n        \"CIFAR10\",\n        \"CIFAR100\",\n    ], \"network type should be ImageNet or CIFAR10 / CIFAR100\"\n    assert depth in [18, 34, 50, 101], \"network depth should be 18, 34, 50 or 101\"\n\n    if depth == 18:\n        model = ResNet(BasicBlock, [2, 2, 2, 2], network_type, num_classes, att_type)\n#         model = ResNet(Bottleneck, [2, 2, 2, 2], network_type, num_classes, att_type)\n\n    elif depth == 34:\n        model = ResNet(BasicBlock, [3, 4, 6, 3], network_type, num_classes, att_type)\n        # model = ResNet(Bottleneck, [3, 4, 6, 3], network_type, num_classes, att_type)\n\n    elif depth == 50:\n        model = ResNet(Bottleneck, [3, 4, 6, 3], network_type, num_classes, att_type)\n\n    elif depth == 101:\n        model = ResNet(Bottleneck, [3, 4, 23, 3], network_type, num_classes, att_type)\n\n    return model\n  \ndef Res2Net(network_type, depth, num_classes, att_type):\n\n    assert network_type in [\n        \"ImageNet\",\n        \"CIFAR10\",\n        \"CIFAR100\",\n    ], \"network type should be ImageNet or CIFAR10 / CIFAR100\"\n    assert depth in [18, 34, 50, 101], \"network depth should be 18, 34, 50 or 101\"\n\n    if depth == 18:\n        model = ResNet(Res2NetBottleneck, [2, 2, 2, 2], network_type, num_classes, att_type)\n\n    elif depth == 34:\n        model = ResNet(Res2NetBottleneck, [3, 4, 6, 3], network_type, num_classes, att_type)\n\n    elif depth == 50:\n        model = ResNet(Res2NetBottleneck, [3, 4, 6, 3], network_type, num_classes, att_type)\n\n    elif depth == 101:\n        model = ResNet(Res2NetBottleneck, [3, 4, 23, 3], network_type, num_classes, att_type)\n\n    return model\ndef ColaNet(network_type, depth, num_classes, att_type):\n\n    assert network_type in [\n        \"ImageNet\",\n        \"CIFAR10\",\n        \"CIFAR100\",\n    ], \"network type should be ImageNet or CIFAR10 / CIFAR100\"\n    assert depth in [18, 34, 50, 101], \"network depth should be 18, 34, 50 or 101\"\n\n    if depth == 18:\n        model = ResNet(ColaBasicBlock, [2, 2, 2, 2], network_type, num_classes, att_type)\n\n    elif depth == 34:\n        model = ResNet(ColaBasicBlock, [3, 4, 6, 3], network_type, num_classes, att_type)\n\n    elif depth == 50:\n        model = ResNet(ColaBasicBlock, [3, 4, 6, 3], network_type, num_classes, att_type)\n\n    elif depth == 101:\n        model = ResNet(ColaBasicBlock, [3, 4, 23, 3], network_type, num_classes, att_type)\n\n    return model\n\ndevice=\"cuda\"\nres2net=Res2Net(\"CIFAR100\",18,100,None).to(device)\nprint(res2net)","metadata":{"execution":{"iopub.status.busy":"2023-05-21T03:28:05.884912Z","iopub.execute_input":"2023-05-21T03:28:05.885316Z","iopub.status.idle":"2023-05-21T03:28:06.281743Z","shell.execute_reply.started":"2023-05-21T03:28:05.885279Z","shell.execute_reply":"2023-05-21T03:28:06.280775Z"},"trusted":true},"execution_count":31,"outputs":[{"name":"stdout","text":"ResNet(\n  (conv1): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n  (relu): ReLU(inplace=True)\n  (layer1): Sequential(\n    (0): Res2NetBottleneck(\n      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): ModuleList(\n        (0-2): 3 x Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (bn2): ModuleList(\n        (0-2): 3 x BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (downsample): Sequential(\n        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): Res2NetBottleneck(\n      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): ModuleList(\n        (0-2): 3 x Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (bn2): ModuleList(\n        (0-2): 3 x BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n  )\n  (layer2): Sequential(\n    (0): Res2NetBottleneck(\n      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): ModuleList(\n        (0-2): 3 x Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (bn2): ModuleList(\n        (0-2): 3 x BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (downsample): Sequential(\n        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): Res2NetBottleneck(\n      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): ModuleList(\n        (0-2): 3 x Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (bn2): ModuleList(\n        (0-2): 3 x BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n  )\n  (layer3): Sequential(\n    (0): Res2NetBottleneck(\n      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): ModuleList(\n        (0-2): 3 x Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (bn2): ModuleList(\n        (0-2): 3 x BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (downsample): Sequential(\n        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): Res2NetBottleneck(\n      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): ModuleList(\n        (0-2): 3 x Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (bn2): ModuleList(\n        (0-2): 3 x BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n  )\n  (layer4): Sequential(\n    (0): Res2NetBottleneck(\n      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): ModuleList(\n        (0-2): 3 x Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (bn2): ModuleList(\n        (0-2): 3 x BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (downsample): Sequential(\n        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): Res2NetBottleneck(\n      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): ModuleList(\n        (0-2): 3 x Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (bn2): ModuleList(\n        (0-2): 3 x BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n  )\n  (fc): Linear(in_features=2048, out_features=100, bias=True)\n)\n","output_type":"stream"}]},{"cell_type":"code","source":"resnet_bottleneck = ResidualNet(\"CIFAR100\",18,100,None).to(device)\nprint(resnet_bottleneck)","metadata":{"execution":{"iopub.status.busy":"2023-05-21T02:16:45.003248Z","iopub.execute_input":"2023-05-21T02:16:45.003595Z","iopub.status.idle":"2023-05-21T02:16:45.404928Z","shell.execute_reply.started":"2023-05-21T02:16:45.003563Z","shell.execute_reply":"2023-05-21T02:16:45.404000Z"},"trusted":true},"execution_count":6,"outputs":[{"name":"stdout","text":"ResNet(\n  (conv1): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n  (relu): ReLU(inplace=True)\n  (layer1): Sequential(\n    (0): Bottleneck(\n      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (downsample): Sequential(\n        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): Bottleneck(\n      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n  )\n  (layer2): Sequential(\n    (0): Bottleneck(\n      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (downsample): Sequential(\n        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): Bottleneck(\n      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n  )\n  (layer3): Sequential(\n    (0): Bottleneck(\n      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (downsample): Sequential(\n        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): Bottleneck(\n      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n  )\n  (layer4): Sequential(\n    (0): Bottleneck(\n      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (downsample): Sequential(\n        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): Bottleneck(\n      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n  )\n  (fc): Linear(in_features=2048, out_features=100, bias=True)\n)\n","output_type":"stream"}]},{"cell_type":"code","source":"resnet_basicblock = ResidualNet(\"CIFAR100\",18,100,None).to(device)\nprint(resnet_basicblock)","metadata":{"execution":{"iopub.status.busy":"2023-05-21T02:43:13.742246Z","iopub.execute_input":"2023-05-21T02:43:13.742599Z","iopub.status.idle":"2023-05-21T02:43:13.974772Z","shell.execute_reply.started":"2023-05-21T02:43:13.742572Z","shell.execute_reply":"2023-05-21T02:43:13.973855Z"},"trusted":true},"execution_count":14,"outputs":[{"name":"stdout","text":"ResNet(\n  (conv1): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n  (relu): ReLU(inplace=True)\n  (layer1): Sequential(\n    (0): BasicBlock(\n      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n    )\n    (1): BasicBlock(\n      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n    )\n  )\n  (layer2): Sequential(\n    (0): BasicBlock(\n      (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (downsample): Sequential(\n        (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): BasicBlock(\n      (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n    )\n  )\n  (layer3): Sequential(\n    (0): BasicBlock(\n      (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (downsample): Sequential(\n        (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): BasicBlock(\n      (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n    )\n  )\n  (layer4): Sequential(\n    (0): BasicBlock(\n      (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (downsample): Sequential(\n        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): BasicBlock(\n      (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n    )\n  )\n  (fc): Linear(in_features=512, out_features=100, bias=True)\n)\n","output_type":"stream"}]},{"cell_type":"code","source":"cola_net = ColaNet(\"CIFAR100\",18,100,None).to(device)\nprint(cola_net)","metadata":{"execution":{"iopub.status.busy":"2023-05-21T03:28:10.903083Z","iopub.execute_input":"2023-05-21T03:28:10.903746Z","iopub.status.idle":"2023-05-21T03:28:11.301178Z","shell.execute_reply.started":"2023-05-21T03:28:10.903715Z","shell.execute_reply":"2023-05-21T03:28:11.300123Z"},"trusted":true},"execution_count":32,"outputs":[{"name":"stdout","text":"ResNet(\n  (conv1): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n  (relu): ReLU(inplace=True)\n  (layer1): Sequential(\n    (0): ColaBasicBlock(\n      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (mix): ModuleList(\n        (0): Conv2d(64, 1, kernel_size=(1, 1), stride=(1, 1), padding=(1, 1), bias=False)\n        (1): BatchNorm2d(1, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (2): Conv2d(1, 64, kernel_size=(1, 1), stride=(1, 1), padding=(1, 1), bias=False)\n        (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (4): ReLU(inplace=True)\n      )\n      (conv2): ModuleList(\n        (0-2): 3 x Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (bn2): ModuleList(\n        (0-2): 3 x BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (downsample): Sequential(\n        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): ColaBasicBlock(\n      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (mix): ModuleList(\n        (0): Conv2d(64, 1, kernel_size=(1, 1), stride=(1, 1), padding=(1, 1), bias=False)\n        (1): BatchNorm2d(1, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (2): Conv2d(1, 64, kernel_size=(1, 1), stride=(1, 1), padding=(1, 1), bias=False)\n        (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (4): ReLU(inplace=True)\n      )\n      (conv2): ModuleList(\n        (0-2): 3 x Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (bn2): ModuleList(\n        (0-2): 3 x BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n  )\n  (layer2): Sequential(\n    (0): ColaBasicBlock(\n      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (mix): ModuleList(\n        (0): Conv2d(128, 1, kernel_size=(1, 1), stride=(1, 1), padding=(1, 1), bias=False)\n        (1): BatchNorm2d(1, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (2): Conv2d(1, 128, kernel_size=(1, 1), stride=(1, 1), padding=(1, 1), bias=False)\n        (3): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (4): ReLU(inplace=True)\n      )\n      (conv2): ModuleList(\n        (0-2): 3 x Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (bn2): ModuleList(\n        (0-2): 3 x BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (downsample): Sequential(\n        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): ColaBasicBlock(\n      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (mix): ModuleList(\n        (0): Conv2d(128, 1, kernel_size=(1, 1), stride=(1, 1), padding=(1, 1), bias=False)\n        (1): BatchNorm2d(1, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (2): Conv2d(1, 128, kernel_size=(1, 1), stride=(1, 1), padding=(1, 1), bias=False)\n        (3): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (4): ReLU(inplace=True)\n      )\n      (conv2): ModuleList(\n        (0-2): 3 x Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (bn2): ModuleList(\n        (0-2): 3 x BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n  )\n  (layer3): Sequential(\n    (0): ColaBasicBlock(\n      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (mix): ModuleList(\n        (0): Conv2d(256, 1, kernel_size=(1, 1), stride=(1, 1), padding=(1, 1), bias=False)\n        (1): BatchNorm2d(1, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (2): Conv2d(1, 256, kernel_size=(1, 1), stride=(1, 1), padding=(1, 1), bias=False)\n        (3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (4): ReLU(inplace=True)\n      )\n      (conv2): ModuleList(\n        (0-2): 3 x Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (bn2): ModuleList(\n        (0-2): 3 x BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (downsample): Sequential(\n        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): ColaBasicBlock(\n      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (mix): ModuleList(\n        (0): Conv2d(256, 1, kernel_size=(1, 1), stride=(1, 1), padding=(1, 1), bias=False)\n        (1): BatchNorm2d(1, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (2): Conv2d(1, 256, kernel_size=(1, 1), stride=(1, 1), padding=(1, 1), bias=False)\n        (3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (4): ReLU(inplace=True)\n      )\n      (conv2): ModuleList(\n        (0-2): 3 x Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (bn2): ModuleList(\n        (0-2): 3 x BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n  )\n  (layer4): Sequential(\n    (0): ColaBasicBlock(\n      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (mix): ModuleList(\n        (0): Conv2d(512, 1, kernel_size=(1, 1), stride=(1, 1), padding=(1, 1), bias=False)\n        (1): BatchNorm2d(1, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (2): Conv2d(1, 512, kernel_size=(1, 1), stride=(1, 1), padding=(1, 1), bias=False)\n        (3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (4): ReLU(inplace=True)\n      )\n      (conv2): ModuleList(\n        (0-2): 3 x Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (bn2): ModuleList(\n        (0-2): 3 x BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (downsample): Sequential(\n        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): ColaBasicBlock(\n      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (mix): ModuleList(\n        (0): Conv2d(512, 1, kernel_size=(1, 1), stride=(1, 1), padding=(1, 1), bias=False)\n        (1): BatchNorm2d(1, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (2): Conv2d(1, 512, kernel_size=(1, 1), stride=(1, 1), padding=(1, 1), bias=False)\n        (3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (4): ReLU(inplace=True)\n      )\n      (conv2): ModuleList(\n        (0-2): 3 x Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (bn2): ModuleList(\n        (0-2): 3 x BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n  )\n  (fc): Linear(in_features=2048, out_features=100, bias=True)\n)\n","output_type":"stream"}]},{"cell_type":"code","source":"import torch.optim as optim\n\ncriterion = nn.CrossEntropyLoss()\n# 这里需要修改优化器\noptimizer = torch.optim.Adam(cola_net.parameters(), lr=0.005)\nprint(len(train_loader))","metadata":{"execution":{"iopub.status.busy":"2023-05-21T03:28:13.288013Z","iopub.execute_input":"2023-05-21T03:28:13.288617Z","iopub.status.idle":"2023-05-21T03:28:13.302009Z","shell.execute_reply.started":"2023-05-21T03:28:13.288584Z","shell.execute_reply":"2023-05-21T03:28:13.301027Z"},"trusted":true},"execution_count":33,"outputs":[{"name":"stdout","text":"1250\n","output_type":"stream"}]},{"cell_type":"code","source":"from tqdm import tqdm\ndef train(net, epoch):\n    net.train()\n    # Loop over each batch from the training set\n    train_tqdm = tqdm(train_loader, desc=\"Epoch \" + str(epoch))\n    for batch_idx, (data, target) in enumerate(train_tqdm):\n        # Copy data to GPU if needed\n        data = data.to(device)\n        target = target.to(device)\n        # Zero gradient buffers\n        optimizer.zero_grad()\n        # Pass data through the network\n        output = net(data)\n        # Calculate loss\n        loss = criterion(output, target)\n        # Backpropagate\n        loss.backward()\n        # Update weights\n        optimizer.step()  # w - alpha * dL / dw\n        train_tqdm.set_postfix({\"loss\": \"%.3g\" % loss.item()})\n    return net\n\ndef validate(net, lossv,top1AccuracyList,top5AccuracyList):\n    net.eval()\n    val_loss = 0\n    top1Correct = 0\n    top5Correct = 0\n    for index,(data, target) in enumerate(test_loader):\n        data = data.to(device)\n        labels = target.to(device)\n        outputs = net(data)\n        val_loss += criterion(outputs, labels).data.item()\n        _, top1Predicted = torch.max(outputs.data, 1)\n        top5Predicted = torch.topk(outputs.data, k=5, dim=1, largest=True)[1]\n        top1Correct += (top1Predicted == labels).cpu().sum().item()\n        label_resize = labels.view(-1, 1).expand_as(top5Predicted)\n        top5Correct += torch.eq(top5Predicted, label_resize).view(-1).cpu().sum().float().item()\n\n    val_loss /= len(test_loader)\n    lossv.append(val_loss)\n\n    top1Acc=100*top1Correct / len(test_loader.dataset)\n    top5Acc=100*top5Correct / len(test_loader.dataset)\n    top1AccuracyList.append(top1Acc)\n    top5AccuracyList.append(top5Acc)\n    \n    print('\\nValidation set: Average loss: {:.4f}, Top1Accuracy: {}/{} ({:.0f}%) Top5Accuracy: {}/{} ({:.0f}%)\\n'.format(\n        val_loss, top1Correct, len(test_loader.dataset), top1Acc,top5Correct, len(test_loader.dataset), top5Acc))\n#     accuracy = 100. * correct.to(torch.float32) / len(testloader.dataset)\n#     accv.append(accuracy)","metadata":{"execution":{"iopub.status.busy":"2023-05-21T03:28:14.529394Z","iopub.execute_input":"2023-05-21T03:28:14.529743Z","iopub.status.idle":"2023-05-21T03:28:14.541885Z","shell.execute_reply.started":"2023-05-21T03:28:14.529715Z","shell.execute_reply":"2023-05-21T03:28:14.540971Z"},"trusted":true},"execution_count":34,"outputs":[]},{"cell_type":"code","source":"import time\n\nres2netLossv = []\nres2netTop1AccuracyList = []   # top1准确率列表\nres2netTop5AccuracyList = []   # top5准确率列表\nmax_epoch = 5\ntraining_time = 0.0\nfor epoch in range(max_epoch):  # loop over the dataset multiple times\n    start = time.time()\n    res2net = train(res2net, epoch)\n    end = time.time()\n    training_time += end-start\n    with torch.no_grad():\n        validate(res2net, res2netLossv,res2netTop1AccuracyList,res2netTop5AccuracyList)\n\n\n        \nprint('Finished Training')\nprint('Training complete in {:.0f}m {:.0f}s'.format(training_time // 60, training_time % 60))\n","metadata":{"execution":{"iopub.status.busy":"2023-05-21T01:49:13.011430Z","iopub.execute_input":"2023-05-21T01:49:13.011841Z","iopub.status.idle":"2023-05-21T02:06:15.828626Z","shell.execute_reply.started":"2023-05-21T01:49:13.011805Z","shell.execute_reply":"2023-05-21T02:06:15.827292Z"},"trusted":true},"execution_count":27,"outputs":[{"name":"stderr","text":"Epoch 0: 100%|██████████| 1250/1250 [03:14<00:00,  6.43it/s, loss=2.34]\n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.3122, Top1Accuracy: 4021/10000 (40%) Top5Accuracy: 7104.0/10000 (71%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 1: 100%|██████████| 1250/1250 [03:14<00:00,  6.44it/s, loss=1.63]\n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.2263, Top1Accuracy: 4265/10000 (43%) Top5Accuracy: 7338.0/10000 (73%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 2: 100%|██████████| 1250/1250 [03:14<00:00,  6.44it/s, loss=1.77] \n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.1583, Top1Accuracy: 4519/10000 (45%) Top5Accuracy: 7467.0/10000 (75%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 3: 100%|██████████| 1250/1250 [03:14<00:00,  6.44it/s, loss=1.59] \n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.1745, Top1Accuracy: 4616/10000 (46%) Top5Accuracy: 7521.0/10000 (75%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 4: 100%|██████████| 1250/1250 [03:14<00:00,  6.44it/s, loss=1.23] \n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.2429, Top1Accuracy: 4666/10000 (47%) Top5Accuracy: 7601.0/10000 (76%)\n\nFinished Training\nTraining complete in 16m 11s\n","output_type":"stream"}]},{"cell_type":"code","source":"import time\n\nresnet_bottleneckLossv = []\nresnet_bottleneckTop1AccuracyList = []   # top1准确率列表\nresnet_bottleneckTop5AccuracyList = []   # top5准确率列表\nmax_epoch = 5\ntraining_time = 0.0\nfor epoch in range(max_epoch):  # loop over the dataset multiple times\n    start = time.time()\n    resnet_bottleneck = train(resnet_bottleneck, epoch)\n    end = time.time()\n    training_time += end-start\n    with torch.no_grad():\n        validate(resnet_bottleneck, resnet_bottleneckLossv,resnet_bottleneckTop1AccuracyList,resnet_bottleneckTop5AccuracyList)\n\n\n        \nprint('Finished Training')\nprint('Training complete in {:.0f}m {:.0f}s'.format(training_time // 60, training_time % 60))","metadata":{"execution":{"iopub.status.busy":"2023-05-21T02:28:46.022872Z","iopub.execute_input":"2023-05-21T02:28:46.023917Z","iopub.status.idle":"2023-05-21T02:35:44.185983Z","shell.execute_reply.started":"2023-05-21T02:28:46.023866Z","shell.execute_reply":"2023-05-21T02:35:44.184783Z"},"trusted":true},"execution_count":10,"outputs":[{"name":"stderr","text":"Epoch 0: 100%|██████████| 1250/1250 [01:16<00:00, 16.41it/s, loss=3.08]\n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 4.4866, Top1Accuracy: 3599/10000 (36%) Top5Accuracy: 6654.0/10000 (67%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 1: 100%|██████████| 1250/1250 [01:15<00:00, 16.48it/s, loss=2.47]\n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.7050, Top1Accuracy: 3805/10000 (38%) Top5Accuracy: 6840.0/10000 (68%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 2: 100%|██████████| 1250/1250 [01:15<00:00, 16.48it/s, loss=2.54]\n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.3556, Top1Accuracy: 4170/10000 (42%) Top5Accuracy: 7197.0/10000 (72%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 3: 100%|██████████| 1250/1250 [01:16<00:00, 16.37it/s, loss=2.09] \n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.2923, Top1Accuracy: 4282/10000 (43%) Top5Accuracy: 7332.0/10000 (73%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 4: 100%|██████████| 1250/1250 [01:15<00:00, 16.56it/s, loss=1.12] \n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.3284, Top1Accuracy: 4342/10000 (43%) Top5Accuracy: 7364.0/10000 (74%)\n\nFinished Training\nTraining complete in 6m 20s\n","output_type":"stream"}]},{"cell_type":"code","source":"import time\n\nresnet_basicblockLossv = []\nresnet_basicblockTop1AccuracyList = []   # top1准确率列表\nresnet_basicblockTop5AccuracyList = []   # top5准确率列表\nmax_epoch = 5\ntraining_time = 0.0\nfor epoch in range(max_epoch):  # loop over the dataset multiple times\n    start = time.time()\n    resnet_basicblock = train(resnet_basicblock, epoch)\n    end = time.time()\n    training_time += end-start\n    with torch.no_grad():\n        validate(resnet_basicblock, resnet_basicblockLossv,resnet_basicblockTop1AccuracyList,resnet_basicblockTop5AccuracyList)\n\n\n        \nprint('Finished Training')\nprint('Training complete in {:.0f}m {:.0f}s'.format(training_time // 60, training_time % 60))","metadata":{"execution":{"iopub.status.busy":"2023-05-21T02:47:46.788808Z","iopub.execute_input":"2023-05-21T02:47:46.789213Z","iopub.status.idle":"2023-05-21T02:51:58.960508Z","shell.execute_reply.started":"2023-05-21T02:47:46.789178Z","shell.execute_reply":"2023-05-21T02:51:58.959190Z"},"trusted":true},"execution_count":18,"outputs":[{"name":"stderr","text":"Epoch 0: 100%|██████████| 1250/1250 [00:43<00:00, 29.07it/s, loss=1.95] \n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.2133, Top1Accuracy: 4380/10000 (44%) Top5Accuracy: 7424.0/10000 (74%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 1: 100%|██████████| 1250/1250 [00:43<00:00, 28.84it/s, loss=1.24] \n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.4084, Top1Accuracy: 4323/10000 (43%) Top5Accuracy: 7323.0/10000 (73%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 2: 100%|██████████| 1250/1250 [00:42<00:00, 29.22it/s, loss=1.14] \n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.6218, Top1Accuracy: 4388/10000 (44%) Top5Accuracy: 7352.0/10000 (74%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 3: 100%|██████████| 1250/1250 [00:43<00:00, 28.68it/s, loss=0.478]\n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 3.0349, Top1Accuracy: 4320/10000 (43%) Top5Accuracy: 7195.0/10000 (72%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 4: 100%|██████████| 1250/1250 [00:43<00:00, 28.54it/s, loss=0.142] \n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 3.0746, Top1Accuracy: 4373/10000 (44%) Top5Accuracy: 7311.0/10000 (73%)\n\nFinished Training\nTraining complete in 3m 37s\n","output_type":"stream"}]},{"cell_type":"code","source":"import time\n\ncola_netLossv = []\ncola_netTop1AccuracyList = []   # top1准确率列表\ncola_netTop5AccuracyList = []   # top5准确率列表\nmax_epoch = 5\ntraining_time = 0.0\nfor epoch in range(max_epoch):  # loop over the dataset multiple times\n    start = time.time()\n    cola_net = train(cola_net, epoch)\n    end = time.time()\n    training_time += end-start\n    with torch.no_grad():\n        validate(cola_net, resnet_basicblockLossv,resnet_basicblockTop1AccuracyList,resnet_basicblockTop5AccuracyList)\n\n\n        \nprint('Finished Training')\nprint('Training complete in {:.0f}m {:.0f}s'.format(training_time // 60, training_time % 60))","metadata":{"execution":{"iopub.status.busy":"2023-05-21T03:35:57.409679Z","iopub.execute_input":"2023-05-21T03:35:57.410113Z","iopub.status.idle":"2023-05-21T03:41:47.943099Z","shell.execute_reply.started":"2023-05-21T03:35:57.410077Z","shell.execute_reply":"2023-05-21T03:41:47.941855Z"},"trusted":true},"execution_count":36,"outputs":[{"name":"stderr","text":"Epoch 0: 100%|██████████| 1250/1250 [01:02<00:00, 20.03it/s, loss=1.91]\n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.2127, Top1Accuracy: 4254/10000 (43%) Top5Accuracy: 7295.0/10000 (73%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 1: 100%|██████████| 1250/1250 [01:02<00:00, 19.97it/s, loss=1.72]\n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.2070, Top1Accuracy: 4538/10000 (45%) Top5Accuracy: 7590.0/10000 (76%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 2: 100%|██████████| 1250/1250 [01:02<00:00, 20.08it/s, loss=1.56] \n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.0643, Top1Accuracy: 4661/10000 (47%) Top5Accuracy: 7641.0/10000 (76%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 3: 100%|██████████| 1250/1250 [01:02<00:00, 20.05it/s, loss=0.873]\n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.0453, Top1Accuracy: 4783/10000 (48%) Top5Accuracy: 7757.0/10000 (78%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 4: 100%|██████████| 1250/1250 [01:02<00:00, 20.11it/s, loss=1.32] \n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.1196, Top1Accuracy: 4807/10000 (48%) Top5Accuracy: 7745.0/10000 (77%)\n\nFinished Training\nTraining complete in 5m 12s\n","output_type":"stream"}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}